Data plays a vital role in today's business analytics environment. Specially, when big data comes under consideration, it becomes a critical task to handle and manage such big data. Also, the big data in its unprocessed state doesn't provide any value which may be useful in the business analytics. For providing the business analytics and deriving business insights from the big data, numerous data analytics techniques are available in the art. Most of the data analytics techniques are based on predictive analytics. In the predictive analytics, by performing statistical analysis of historical or past data probable future possibilities can be predicted for an event or situation occurring in a business environment.
The probable future possibilities predicted in the predictive analytics may indicate possible risks or opportunities in the future. Based on the risks or the opportunities predicted, business personnel may have to take decisions manually. Such manual decisions are often not comprehensive and reliable. Further, no support is provided in taking decisions based on the future possibilities predicted. Decisions are generally made to choose a right or correct strategy which can improve the future possibilities predicted. However, the predictive analytics are limited to providing only future possibilities or future outcomes, and hence are not able to provide decisions for taking advantage from the opportunities or mitigate the risks predicted as the future possibilities. Therefore, the predictive analytics are not of much value unless they support decision making process. Hence, there is a long-felt need for methods and systems that would help in deriving business decisions based on data analytics.